Nos derniers articles

This report uses UK fire statistics to model insurance claims for a company next year. It estimates the total sum of claims by modeling both the number and size of fires as random variables from statistical distributions. Monte Carlo simulations in R are used to predict the probability distribution of total claim costs.

Accurate insurance claims forecasting is vital for financial planning and risk management. This study introduces innovative variables, such as weather conditions and car sales, and employs Machine Learning algorithms to predict average insurance claims per quarter. Key influential variables include new car sales and minimum temperature with specific lags. The findings aid insurers in enhancing claims forecasting by considering additional parameters like weather and sales data.

"We study a #reinsurer who faces multiple sources of #model #uncertainty. The reinsurer offers contracts to n #insurers whose #claims follow different compound #poisson processes. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an #entropy #penalty…."

This article highlights the importance of #cybersecurity in contemporary business models and the need for #legal practitioners and #it professionals to work together to assess the extent to which #privacy and #security measures qualify as "appropriate" in the context of #liability #claims and actions for #damages. The article provides guidance on how to move from technical insight to legal #compliance.

Key findings:1. #masstort plaintiff lawyers do not primarily base their litigation and settlement strategy on defendants' #liabilityinsurance, except in cases of insolvency.2. Despite #insurance policies assigning control over defense to the #insurer, mass tort defendants typically retain control over their defense, even when they recover under these policies.3. Mass tort defendants usually use their own funds to settle #claims, seeking indemnification from liability insurers, if available, at a later stage.4. Many mass tort plaintiff law firms rely on non-recourse litigation funding, reminiscent of early forms of commercial insurance like bottomry and respondentia. An emerging insurance market is reducing the cost of this funding and may eventually replace it.

This study examines the use of #artificialintelligence (#ai) and #bigdata data analytics by #insurers in #belgium for segmentation purposes to determine #claims#probability for prospective policyholders. The implementation of AI and big data analytics can benefit insurers by increasing the accuracy of #riskassessment. However, pervasive segmentation can have negative implications and potentially harm policyholders if their risk is incorrectly calculated. Existing restrictions in #insurance#regulations fall short of protecting policyholders from inaccuracies in risk assessments, potentially resulting in incorrect #premiums or conditions.

#audit is increasingly being called upon to provide assurance on #esg#claims made by their clients, in addition to assessing the accuracy of financial numbers. The concept of #doublemateriality, which involves assessing materiality from both financial and non-financial aspects, is becoming increasingly important in audits. This paper proposes an audit approach that incorporates a “double materiality” lens of financial and ESG data to evaluate the #compliancerisk of ESG #disclosure.

"In this paper, a compound Poisson risk model with two-sided jumps and proportional investment is considered. The downward jumps represent the claims, while the upward jumps are also allowed to represent the random gains."

"... this paper proposes a new risk variable elimination method as well as a real-time road risk model design framework and concludes that claim history will be regarded as a "noise" factor and deprecated in the Pay-How-You-Drive model."

"... we develop a granular occurrence and development model for non-life claims that allows to resolve the inconsistency in traditional pricing techniques between actual, complete observations on the one hand and best estimates on the other hand. We illustrate our proposed model on a reinsurance portfolio, where large uncertainties in the best estimates originate from long reporting and settlement delays, low claim frequencies and heavy (even extreme) claim sizes."